{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Experiment: \n",
    "\n",
    "Evaluate pruning by magnitude weighted by coactivations (more thorough evaluation), compare it to baseline (SET), in GSC. Applied only to linear layers\n",
    "\n",
    "Search through different percentage\n",
    "\n",
    "#### Motivation.\n",
    "\n",
    "Check if results are consistently above baseline.\n",
    "\n",
    "#### Conclusion\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "base_exp_config = dict(\n",
    "    device=\"cuda\",\n",
    "    # dataset related\n",
    "    dataset_name=\"PreprocessedGSC\",\n",
    "    data_dir=os.path.expanduser(\"~/nta/datasets/gsc\"),\n",
    "    batch_size_train=(4, 16),\n",
    "    batch_size_test=1000,\n",
    "    # network related\n",
    "    network=\"GSCHeb\",\n",
    "    optim_alg=\"SGD\",\n",
    "    momentum=0.9, # 0,\n",
    "    learning_rate=0.01, # 0.1,\n",
    "    weight_decay= 0.01, # 1e-4,\n",
    "    lr_scheduler=\"MultiStepLR\",\n",
    "    lr_milestones=[30, 60, 90],\n",
    "    lr_gamma=0.9, # 0.1,\n",
    "    use_kwinners=True,\n",
    "    # sparse_linear_only=True, # False\n",
    "    # model related\n",
    "    model=tune.grid_search([\"DSNNWeightedMag\", \"DSNNMixedHeb\"]),\n",
    "    on_perc=0.04,\n",
    "    # sparse related\n",
    "    hebbian_prune_perc=None,\n",
    "    hebbian_grow=False,\n",
    "    weight_prune_perc=tune.grid_search(list(np.arange(0, 1.001, 0.05))),\n",
    "    pruning_early_stop=2,\n",
    "    # additional validation\n",
    "    test_noise=False,\n",
    "    # debugging\n",
    "    debug_weights=True,\n",
    "    debug_sparse=True,\n",
    ")\n",
    "\n",
    "# ray configurations\n",
    "tune_config = dict(\n",
    "    name=__file__.replace(\".py\", \"\") + \"_searchperc\",\n",
    "    num_samples=5,\n",
    "    local_dir=os.path.expanduser(\"~/nta/results\"),\n",
    "    checkpoint_freq=0,\n",
    "    checkpoint_at_end=False,\n",
    "    stop={\"training_iteration\": 100},\n",
    "    resources_per_trial={\"cpu\": 1, \"gpu\": .20},\n",
    "    loggers=DEFAULT_LOGGERS,\n",
    "    verbose=0,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import absolute_import\n",
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import tabulate\n",
    "import pprint\n",
    "import click\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from ray.tune.commands import *\n",
    "from nupic.research.frameworks.dynamic_sparse.common.browser import *\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set(style=\"whitegrid\")\n",
    "sns.set_palette(\"colorblind\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "matplotlib.rc('xtick', labelsize=14)\n",
    "matplotlib.rc('ytick', labelsize=14)\n",
    "plt.rcParams.update({'font.size': 14})\n",
    "plt.rcParams.update({\"axes.grid\": True, \"grid.linewidth\": 0.5})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and check data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "improved_mag_gsc_searchperc2\n"
     ]
    }
   ],
   "source": [
    "exps = ['improved_mag_gsc_searchperc2']\n",
    "paths = [os.path.expanduser(\"~/nta/results/{}\".format(e)) for e in exps]\n",
    "df = load_many(paths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Experiment Name</th>\n",
       "      <th>train_acc_max</th>\n",
       "      <th>train_acc_max_epoch</th>\n",
       "      <th>train_acc_min</th>\n",
       "      <th>train_acc_min_epoch</th>\n",
       "      <th>train_acc_median</th>\n",
       "      <th>train_acc_last</th>\n",
       "      <th>val_acc_max</th>\n",
       "      <th>val_acc_max_epoch</th>\n",
       "      <th>val_acc_min</th>\n",
       "      <th>...</th>\n",
       "      <th>model</th>\n",
       "      <th>momentum</th>\n",
       "      <th>network</th>\n",
       "      <th>on_perc</th>\n",
       "      <th>optim_alg</th>\n",
       "      <th>pruning_early_stop</th>\n",
       "      <th>test_noise</th>\n",
       "      <th>use_kwinners</th>\n",
       "      <th>weight_decay</th>\n",
       "      <th>weight_prune_perc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0_model=DSNNWeightedMag,weight_prune_perc=0.0</td>\n",
       "      <td>0.852798</td>\n",
       "      <td>98</td>\n",
       "      <td>0.269407</td>\n",
       "      <td>0</td>\n",
       "      <td>0.834196</td>\n",
       "      <td>0.850845</td>\n",
       "      <td>0.912618</td>\n",
       "      <td>96</td>\n",
       "      <td>0.405564</td>\n",
       "      <td>...</td>\n",
       "      <td>DSNNWeightedMag</td>\n",
       "      <td>0</td>\n",
       "      <td>GSCHeb</td>\n",
       "      <td>0.04</td>\n",
       "      <td>SGD</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1_model=DSNNMixedHeb,weight_prune_perc=0.0</td>\n",
       "      <td>0.847818</td>\n",
       "      <td>98</td>\n",
       "      <td>0.281906</td>\n",
       "      <td>0</td>\n",
       "      <td>0.830412</td>\n",
       "      <td>0.845230</td>\n",
       "      <td>0.905564</td>\n",
       "      <td>84</td>\n",
       "      <td>0.388323</td>\n",
       "      <td>...</td>\n",
       "      <td>DSNNMixedHeb</td>\n",
       "      <td>0</td>\n",
       "      <td>GSCHeb</td>\n",
       "      <td>0.04</td>\n",
       "      <td>SGD</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2_model=DSNNWeightedMag,weight_prune_perc=0.05</td>\n",
       "      <td>0.890099</td>\n",
       "      <td>95</td>\n",
       "      <td>0.296016</td>\n",
       "      <td>0</td>\n",
       "      <td>0.874890</td>\n",
       "      <td>0.889610</td>\n",
       "      <td>0.927900</td>\n",
       "      <td>88</td>\n",
       "      <td>0.483934</td>\n",
       "      <td>...</td>\n",
       "      <td>DSNNWeightedMag</td>\n",
       "      <td>0</td>\n",
       "      <td>GSCHeb</td>\n",
       "      <td>0.04</td>\n",
       "      <td>SGD</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3_model=DSNNMixedHeb,weight_prune_perc=0.05</td>\n",
       "      <td>0.888097</td>\n",
       "      <td>96</td>\n",
       "      <td>0.298604</td>\n",
       "      <td>0</td>\n",
       "      <td>0.872766</td>\n",
       "      <td>0.887267</td>\n",
       "      <td>0.932210</td>\n",
       "      <td>88</td>\n",
       "      <td>0.471395</td>\n",
       "      <td>...</td>\n",
       "      <td>DSNNMixedHeb</td>\n",
       "      <td>0</td>\n",
       "      <td>GSCHeb</td>\n",
       "      <td>0.04</td>\n",
       "      <td>SGD</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4_model=DSNNWeightedMag,weight_prune_perc=0.1</td>\n",
       "      <td>0.878772</td>\n",
       "      <td>95</td>\n",
       "      <td>0.273460</td>\n",
       "      <td>0</td>\n",
       "      <td>0.863807</td>\n",
       "      <td>0.872913</td>\n",
       "      <td>0.930251</td>\n",
       "      <td>90</td>\n",
       "      <td>0.432210</td>\n",
       "      <td>...</td>\n",
       "      <td>DSNNWeightedMag</td>\n",
       "      <td>0</td>\n",
       "      <td>GSCHeb</td>\n",
       "      <td>0.04</td>\n",
       "      <td>SGD</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Experiment Name  train_acc_max  \\\n",
       "0   0_model=DSNNWeightedMag,weight_prune_perc=0.0       0.852798   \n",
       "1      1_model=DSNNMixedHeb,weight_prune_perc=0.0       0.847818   \n",
       "2  2_model=DSNNWeightedMag,weight_prune_perc=0.05       0.890099   \n",
       "3     3_model=DSNNMixedHeb,weight_prune_perc=0.05       0.888097   \n",
       "4   4_model=DSNNWeightedMag,weight_prune_perc=0.1       0.878772   \n",
       "\n",
       "   train_acc_max_epoch  train_acc_min  train_acc_min_epoch  train_acc_median  \\\n",
       "0                   98       0.269407                    0          0.834196   \n",
       "1                   98       0.281906                    0          0.830412   \n",
       "2                   95       0.296016                    0          0.874890   \n",
       "3                   96       0.298604                    0          0.872766   \n",
       "4                   95       0.273460                    0          0.863807   \n",
       "\n",
       "   train_acc_last  val_acc_max  val_acc_max_epoch  val_acc_min  ...  \\\n",
       "0        0.850845     0.912618                 96     0.405564  ...   \n",
       "1        0.845230     0.905564                 84     0.388323  ...   \n",
       "2        0.889610     0.927900                 88     0.483934  ...   \n",
       "3        0.887267     0.932210                 88     0.471395  ...   \n",
       "4        0.872913     0.930251                 90     0.432210  ...   \n",
       "\n",
       "             model  momentum  network on_perc  optim_alg pruning_early_stop  \\\n",
       "0  DSNNWeightedMag         0   GSCHeb    0.04        SGD                  2   \n",
       "1     DSNNMixedHeb         0   GSCHeb    0.04        SGD                  2   \n",
       "2  DSNNWeightedMag         0   GSCHeb    0.04        SGD                  2   \n",
       "3     DSNNMixedHeb         0   GSCHeb    0.04        SGD                  2   \n",
       "4  DSNNWeightedMag         0   GSCHeb    0.04        SGD                  2   \n",
       "\n",
       "  test_noise  use_kwinners  weight_decay weight_prune_perc  \n",
       "0      False          True          0.01              0.00  \n",
       "1      False          True          0.01              0.00  \n",
       "2      False          True          0.01              0.05  \n",
       "3      False          True          0.01              0.05  \n",
       "4      False          True          0.01              0.10  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace hebbian prine\n",
    "df['hebbian_prune_perc'] = df['hebbian_prune_perc'].replace(np.nan, 0.0, regex=True)\n",
    "df['weight_prune_perc'] = df['weight_prune_perc'].replace(np.nan, 0.0, regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Experiment Name', 'train_acc_max', 'train_acc_max_epoch',\n",
       "       'train_acc_min', 'train_acc_min_epoch', 'train_acc_median',\n",
       "       'train_acc_last', 'val_acc_max', 'val_acc_max_epoch', 'val_acc_min',\n",
       "       'val_acc_min_epoch', 'val_acc_median', 'val_acc_last', 'val_acc_all',\n",
       "       'epochs', 'experiment_file_name', 'experiment_base_path', 'trial_time',\n",
       "       'mean_epoch_time', 'scatter_plot_dicts', 'batch_size_test',\n",
       "       'batch_size_train', 'data_dir', 'dataset_name', 'debug_sparse',\n",
       "       'debug_weights', 'device', 'hebbian_grow', 'hebbian_prune_perc',\n",
       "       'learning_rate', 'lr_gamma', 'lr_milestones', 'lr_scheduler', 'model',\n",
       "       'momentum', 'network', 'on_perc', 'optim_alg', 'pruning_early_stop',\n",
       "       'test_noise', 'use_kwinners', 'weight_decay', 'weight_prune_perc'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(160, 43)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Experiment Name                1_model=DSNNMixedHeb,weight_prune_perc=0.0\n",
       "train_acc_max                                                    0.847818\n",
       "train_acc_max_epoch                                                    98\n",
       "train_acc_min                                                    0.281906\n",
       "train_acc_min_epoch                                                     0\n",
       "train_acc_median                                                 0.830412\n",
       "train_acc_last                                                    0.84523\n",
       "val_acc_max                                                      0.905564\n",
       "val_acc_max_epoch                                                      84\n",
       "val_acc_min                                                      0.388323\n",
       "val_acc_min_epoch                                                       0\n",
       "val_acc_median                                                   0.878723\n",
       "val_acc_last                                                     0.891066\n",
       "val_acc_all             0     0.388323\n",
       "1     0.738636\n",
       "2     0.791144\n",
       "3...\n",
       "epochs                                                                100\n",
       "experiment_file_name    /Users/mcaporale/nta/results/improved_mag_gsc_...\n",
       "experiment_base_path                         improved_mag_gsc_searchperc2\n",
       "trial_time                                                        33.5881\n",
       "mean_epoch_time                                                  0.335881\n",
       "scatter_plot_dicts                                                     {}\n",
       "batch_size_test                                                      1000\n",
       "batch_size_train                                                       10\n",
       "data_dir                                    /home/ubuntu/nta/datasets/gsc\n",
       "dataset_name                                              PreprocessedGSC\n",
       "debug_sparse                                                         True\n",
       "debug_weights                                                        True\n",
       "device                                                               cuda\n",
       "hebbian_grow                                                        False\n",
       "hebbian_prune_perc                                                      0\n",
       "learning_rate                                                        0.01\n",
       "lr_gamma                                                              0.9\n",
       "lr_milestones                                                          60\n",
       "lr_scheduler                                                  MultiStepLR\n",
       "model                                                        DSNNMixedHeb\n",
       "momentum                                                                0\n",
       "network                                                            GSCHeb\n",
       "on_perc                                                              0.04\n",
       "optim_alg                                                             SGD\n",
       "pruning_early_stop                                                      2\n",
       "test_noise                                                          False\n",
       "use_kwinners                                                         True\n",
       "weight_decay                                                         0.01\n",
       "weight_prune_perc                                                       0\n",
       "Name: 1, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model\n",
       "DSNNMixedHeb       80\n",
       "DSNNWeightedMag    80\n",
       "Name: model, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('model')['model'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Experiment Details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Did any  trials failed?\n",
    "df[df[\"epochs\"]<30][\"epochs\"].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(152, 43)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing failed or incomplete trials\n",
    "df_origin = df.copy()\n",
    "df = df_origin[df_origin[\"epochs\"]>=30]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "152    6\n",
       "153    4\n",
       "154    3\n",
       "155    1\n",
       "156    2\n",
       "157    2\n",
       "158    1\n",
       "159    1\n",
       "Name: epochs, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# which ones failed?\n",
    "# failed, or still ongoing?\n",
    "df_origin['failed'] = df_origin[\"epochs\"]<30\n",
    "df_origin[df_origin['failed']]['epochs']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# helper functions\n",
    "def mean_and_std(s):\n",
    "    return \"{:.3f} ± {:.3f}\".format(s.mean(), s.std())\n",
    "\n",
    "def round_mean(s):\n",
    "    return \"{:.0f}\".format(round(s.mean()))\n",
    "\n",
    "stats = ['min', 'max', 'mean', 'std']\n",
    "\n",
    "def agg(columns, filter=None, round=3):\n",
    "    if filter is None:\n",
    "        return (df.groupby(columns)\n",
    "             .agg({'val_acc_max_epoch': round_mean,\n",
    "                   'val_acc_max': stats,                \n",
    "                   'model': ['count']})).round(round)\n",
    "    else:\n",
    "        return (df[filter].groupby(columns)\n",
    "             .agg({'val_acc_max_epoch': round_mean,\n",
    "                   'val_acc_max': stats,                \n",
    "                   'model': ['count']})).round(round)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Does improved weight pruning outperforms regular SET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>val_acc_max_epoch</th>\n",
       "      <th colspan=\"4\" halign=\"left\">val_acc_max</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>round_mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DSNNMixedHeb</th>\n",
       "      <td>38</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.936</td>\n",
       "      <td>0.850</td>\n",
       "      <td>0.114</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>63</td>\n",
       "      <td>0.405</td>\n",
       "      <td>0.933</td>\n",
       "      <td>0.878</td>\n",
       "      <td>0.101</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                val_acc_max_epoch val_acc_max                      model\n",
       "                       round_mean         min    max   mean    std count\n",
       "model                                                                   \n",
       "DSNNMixedHeb                   38       0.400  0.936  0.850  0.114    76\n",
       "DSNNWeightedMag                63       0.405  0.933  0.878  0.101    76"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg(['model'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weight_prune_perc</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.00</th>\n",
       "      <td>82</td>\n",
       "      <td>0.899</td>\n",
       "      <td>0.913</td>\n",
       "      <td>0.908</td>\n",
       "      <td>0.005</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.05</th>\n",
       "      <td>76</td>\n",
       "      <td>0.920</td>\n",
       "      <td>0.932</td>\n",
       "      <td>0.928</td>\n",
       "      <td>0.004</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.10</th>\n",
       "      <td>81</td>\n",
       "      <td>0.917</td>\n",
       "      <td>0.930</td>\n",
       "      <td>0.926</td>\n",
       "      <td>0.004</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.15</th>\n",
       "      <td>78</td>\n",
       "      <td>0.920</td>\n",
       "      <td>0.936</td>\n",
       "      <td>0.929</td>\n",
       "      <td>0.005</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.20</th>\n",
       "      <td>72</td>\n",
       "      <td>0.915</td>\n",
       "      <td>0.935</td>\n",
       "      <td>0.926</td>\n",
       "      <td>0.007</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>85</td>\n",
       "      <td>0.912</td>\n",
       "      <td>0.929</td>\n",
       "      <td>0.924</td>\n",
       "      <td>0.006</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.30</th>\n",
       "      <td>70</td>\n",
       "      <td>0.905</td>\n",
       "      <td>0.932</td>\n",
       "      <td>0.919</td>\n",
       "      <td>0.008</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.35</th>\n",
       "      <td>62</td>\n",
       "      <td>0.909</td>\n",
       "      <td>0.927</td>\n",
       "      <td>0.918</td>\n",
       "      <td>0.007</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.40</th>\n",
       "      <td>53</td>\n",
       "      <td>0.899</td>\n",
       "      <td>0.929</td>\n",
       "      <td>0.911</td>\n",
       "      <td>0.011</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.45</th>\n",
       "      <td>53</td>\n",
       "      <td>0.888</td>\n",
       "      <td>0.931</td>\n",
       "      <td>0.905</td>\n",
       "      <td>0.017</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>45</td>\n",
       "      <td>0.874</td>\n",
       "      <td>0.923</td>\n",
       "      <td>0.902</td>\n",
       "      <td>0.020</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.55</th>\n",
       "      <td>53</td>\n",
       "      <td>0.861</td>\n",
       "      <td>0.922</td>\n",
       "      <td>0.895</td>\n",
       "      <td>0.024</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.60</th>\n",
       "      <td>49</td>\n",
       "      <td>0.860</td>\n",
       "      <td>0.915</td>\n",
       "      <td>0.893</td>\n",
       "      <td>0.023</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.65</th>\n",
       "      <td>50</td>\n",
       "      <td>0.839</td>\n",
       "      <td>0.913</td>\n",
       "      <td>0.879</td>\n",
       "      <td>0.031</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.70</th>\n",
       "      <td>39</td>\n",
       "      <td>0.802</td>\n",
       "      <td>0.896</td>\n",
       "      <td>0.863</td>\n",
       "      <td>0.035</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <td>18</td>\n",
       "      <td>0.784</td>\n",
       "      <td>0.883</td>\n",
       "      <td>0.839</td>\n",
       "      <td>0.044</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.80</th>\n",
       "      <td>14</td>\n",
       "      <td>0.756</td>\n",
       "      <td>0.862</td>\n",
       "      <td>0.810</td>\n",
       "      <td>0.042</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.85</th>\n",
       "      <td>9</td>\n",
       "      <td>0.743</td>\n",
       "      <td>0.828</td>\n",
       "      <td>0.790</td>\n",
       "      <td>0.040</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.90</th>\n",
       "      <td>3</td>\n",
       "      <td>0.642</td>\n",
       "      <td>0.774</td>\n",
       "      <td>0.729</td>\n",
       "      <td>0.049</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.95</th>\n",
       "      <td>1</td>\n",
       "      <td>0.652</td>\n",
       "      <td>0.761</td>\n",
       "      <td>0.692</td>\n",
       "      <td>0.047</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.00</th>\n",
       "      <td>0</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.498</td>\n",
       "      <td>0.444</td>\n",
       "      <td>0.047</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  val_acc_max_epoch val_acc_max                      model\n",
       "                         round_mean         min    max   mean    std count\n",
       "weight_prune_perc                                                         \n",
       "0.00                             82       0.899  0.913  0.908  0.005     8\n",
       "0.05                             76       0.920  0.932  0.928  0.004     8\n",
       "0.10                             81       0.917  0.930  0.926  0.004     8\n",
       "0.15                             78       0.920  0.936  0.929  0.005     8\n",
       "0.20                             72       0.915  0.935  0.926  0.007     8\n",
       "0.25                             85       0.912  0.929  0.924  0.006     8\n",
       "0.30                             70       0.905  0.932  0.919  0.008     8\n",
       "0.35                             62       0.909  0.927  0.918  0.007     8\n",
       "0.40                             53       0.899  0.929  0.911  0.011     8\n",
       "0.45                             53       0.888  0.931  0.905  0.017     8\n",
       "0.50                             45       0.874  0.923  0.902  0.020     8\n",
       "0.55                             53       0.861  0.922  0.895  0.024     8\n",
       "0.60                             49       0.860  0.915  0.893  0.023     8\n",
       "0.65                             50       0.839  0.913  0.879  0.031     6\n",
       "0.70                             39       0.802  0.896  0.863  0.035     6\n",
       "0.75                             18       0.784  0.883  0.839  0.044     6\n",
       "0.80                             14       0.756  0.862  0.810  0.042     6\n",
       "0.85                              9       0.743  0.828  0.790  0.040     6\n",
       "0.90                              3       0.642  0.774  0.729  0.049     6\n",
       "0.95                              1       0.652  0.761  0.692  0.047     6\n",
       "1.00                              0       0.400  0.498  0.444  0.047     6"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg(['weight_prune_perc'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Results not in line with what the previous experiment shows - what changed?**\n",
    "- All hyperparameters are set to the same values - expected similar results\n",
    "- Any relevant changes to the code?\n",
    "\n",
    "Edit: yes, momentum is set to 0.9, while in the original experiments is set to 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>val_acc_max_epoch</th>\n",
       "      <th colspan=\"4\" halign=\"left\">val_acc_max</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>round_mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>on_perc</th>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">0.04</th>\n",
       "      <th>DSNNMixedHeb</th>\n",
       "      <td>38</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.936</td>\n",
       "      <td>0.850</td>\n",
       "      <td>0.114</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>63</td>\n",
       "      <td>0.405</td>\n",
       "      <td>0.933</td>\n",
       "      <td>0.878</td>\n",
       "      <td>0.101</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        val_acc_max_epoch val_acc_max                       \\\n",
       "                               round_mean         min    max   mean    std   \n",
       "on_perc model                                                                \n",
       "0.04    DSNNMixedHeb                   38       0.400  0.936  0.850  0.114   \n",
       "        DSNNWeightedMag                63       0.405  0.933  0.878  0.101   \n",
       "\n",
       "                        model  \n",
       "                        count  \n",
       "on_perc model                  \n",
       "0.04    DSNNMixedHeb       76  \n",
       "        DSNNWeightedMag    76  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg(['on_perc', 'model'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# translate model names\n",
    "rcParams['figure.figsize'] = 16, 8\n",
    "d = {\n",
    "    'DSNNWeightedMag': 'Dynamic Sparse Neural Network (DSNN)',\n",
    "    'DSNNMixedHeb': 'Sparse Evolutionary Training (SET)',\n",
    "    'SparseModel': 'Static',        \n",
    "}\n",
    "df_plot = df.copy()\n",
    "df_plot['model'] = df_plot['model'].apply(lambda x: d[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_for_paper():\n",
    "    rcParams['figure.figsize'] = 10,6\n",
    "    matplotlib.rc('xtick', labelsize=14)\n",
    "    matplotlib.rc('ytick', labelsize=14)\n",
    "    matplotlib.rc('ytick', labelsize=14)\n",
    "    plt.rcParams.update({'axes.labelsize': 14})\n",
    "    plt.rcParams.update({'legend.fontsize': 14, 'legend.loc': 'lower left'})\n",
    "    plt.rcParams.update({'font.size': 14})\n",
    "    plt.rcParams.update({\"axes.grid\": True, \"grid.linewidth\": 0.5})\n",
    "    plt.rcParams.update({'font.family': 'serif'})\n",
    "    plt.rcParams.update({'text.usetex': True})    \n",
    "plot_for_paper()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 383,
       "width": 615
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sns.scatterplot(data=df_plot, x='weight_prune_perc', y='val_acc_max', hue='model')\n",
    "fig, ax = plt.subplots()\n",
    "sns.lineplot(data=df_plot, x='weight_prune_perc', y='val_acc_max', hue='model')\n",
    "plt.xlabel(\"% of weights pruned and grown at each epoch ($\\\\beta$)\")\n",
    "plt.ylabel(\"test accuracy\")\n",
    "plt.ylim((0.4,1.0))\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles=handles[1:], labels=labels[1:])\n",
    "fig_path = r\"~/Google Drive/papers/dynamic_sparse/figures/neurips_2019/pruning_percentage.png\"\n",
    "fig_path = os.path.expanduser(fig_path)\n",
    "plt.savefig(fig_path, dpi=1000, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rcParams['figure.figsize'] = 16, 8\n",
    "filter = df_plot['model'] != 'Static'\n",
    "sns.lineplot(data=df_plot[filter], x='weight_prune_perc', y='val_acc_max_epoch', hue='model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Acc where it reaches the max validation accuracy Consistently decreases in the Weight Magnitude - even in cases where the final acc is higher, as in between [0,0.2] of weight_prune_perc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.lineplot(data=df_plot, x='weight_prune_perc', y='val_acc_last', hue='model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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